; Preservice Teachers Personality_ Motives_ Motivation_ and Attitudes
Learning Center
Plans & pricing Sign in
Sign Out
Your Federal Quarterly Tax Payments are due April 15th Get Help Now >>

Preservice Teachers Personality_ Motives_ Motivation_ and Attitudes


  • pg 1
									       Preservice Teachers’ Personality, Motives, Motivation, and Attitudes
        Associated with the Use of Social Network Services: Facebook Case

                                             Oğuzhan ATABEK
                         Department of Computer Education and Instructional Technology
                                             Akdeniz University

             Abstract: The paper reports the findings from the quantitative part of an ongoing
             research study on Turkish preservice teachers who use Facebook (FB). Social
             network services are increasing in popularity and certain social network services are
             already being used for educational purposes. Social network services simulate and
             may even emulate existing social networks on an abstract level and reflect them in
             the electronic world. The social structures that they rely on are already being studied
             in regard to the context of learning and education. But the electronic simulations or
             reflections of existing social structures need further explorations. To investigate the
             personality, motivation, motives, and attitude factors that influence FB use, 641
             preservice teachers who were students of Middle East Technical University (METU)
             in Turkey were surveyed. Four regression analyses were used to describe the results
             of the collected data.

The movie of the year in 2010 was “The Social Network,” which was the last feature film of accomplished
American film director David Fincher. Fincher’s movie tells the story of Harvard computer science undergrad Mark
Zuckerberg who founded FB in 2003. This “provocative” movie delves into “the impetus for invention, the changing
face of social interaction and the limits of friendship — the old-fashioned kind and the version linking 500 million
Facebook users” (Puig, 2010). According to Hornaday (2010), the movie depicts “a man who changes society
through bending an emergent technology to his will.” Interestingly, Rickey (2010) defines the movie correctly by
stating that “it is the improbably entertaining story of how new media are altering the very nature of courtship and
friendship.” Wilson (2010) defines the movie as “[a] film that's at once timely and timeless.” Apparently, the genius
director is credited with a serious portion of the success and popularity of the movie. But the cause of the popularity
of the movie and the excitement it generates isn’t limited to the people behind it. What makes watching this film
“the event of the year” actually is its subject: Facebook. Obviously, being the focal center of it, FB makes a movie a
worldwide social event. With this in mind, it is not hard to understand the tremendous impact of FB on society. The
world economy informs us about FB, and its impact on the society, as well. World Economic Forum’s “The Global
Information Technology Report” depicts FB as a major player in the world economy which grew at a brisk pace
even in the economic crisis (Dutta et al., 2010, p. 3). It indicates that “social networking and Web 2.0 companies
such as Facebook” emerged as the “major segment” of information and communication technologies (ICT)
throughout 2008, even while the core subsectors, such as semiconductors have suffered. As a consequence of its
economic success, FB leads all online publishers with a 23% share in the market for display ads (Lipsman, 2010).

Computer networks, once considered the “hard side” of computing, are now utilized for helping bring the private
lives of individuals to online social networks services. And these social network services (SNS) are increasing in
their importance as they become part of many people’s daily lives. Being used for many purposes, SNSs can also
serve as educational applications. The National School Boards Association, representing 95,000 local school board
members across the United States, has released a report on student use of social networking and reported that 59%
of students who use social networking say they talk about education related topics (NSBA, 2007, p. 1). More
significantly, 50% of those students who use social networking say they talk specifically about schoolwork.
Considering those connotations of FB regarding its impact such as: invention, social interaction, changing the face
of social interaction, changing society by a technology, altering the nature of interpersonal relationship; one can
easily come up with serious questions about “this society changing” and “human nature altering” technology
regarding learning, teaching, and education.

SNSs are a relatively new phenomena and related literature is limited. Given the overwhelming popularity of FB, its
profound social and cultural impact, and its potential for educational implications, this paper aims to extend the
existing literature by reporting the findings from the quantitative part of an ongoing research study on Turkish
preservice teachers who use FB. The purpose of the study is to provide scholars and professionals in the field of
educational technology and teacher training community with useful information by investigating the nature of FB
use among preservice teachers and finding out personality, motives, motivational and attitudinal factors that
influence its use.

An SNS is an Internet website that highly invests in social networks. Those services represent and recreate an actual
social network among the users of the service on the internet, and add many features that facilitate the relationships
of those users. SNS can be defined as “web-based services that allow individuals to (1) construct a public or
semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection,
and (3) view and traverse their list of connections and those made by others within the system” (Boyd & Ellison,
2008, p. 211). A social network (SN), which is a critical concept for FB as for other SNSs, is a social structure that
is comprised of individuals who have relationships among each other. An SN is made up of those individuals, their
relationships, attributes of those relationships, and properties of those individuals such as personality and

There are numerous SNSs varying in the number of their users, focus of the site, or the geographical area in which
the site is popular. Among many SNSs having millions of users, (FB) is “overwhelmingly more popular” (Kirschner
& Karpinski, 2010, p. 1239). It has “more than 800 million active users” and “50% of [its] active users log on to
Facebook in any given day” (Facebook, 2012). Even though it has users from every age group; FB “remains
primarily a college-age and emerging adult phenomenon” (Kirschner & Karpinski, 2010, p. 1239). As previously
highlighted, the Internet defines the nature of SNSs and, thus, Internet makes them accessible “anytime, anywhere.”
Being almost infinitely accessible, information exchange works extremely well via SNSs. Taking into consideration
how previous technologies changed how people communicated, FB has the potential to change how people behave.

Considering it a hypermedia internet application, for FB, therefore, personality is a core concept. Personality may be
defined as “important and relatively stable characteristics within a person that account for consistent patterns of
behavior” (Ewen, 2003, p.5). Among many others, there is also “The Five Factor Model” (FFM). FFM is a
hierarchical organization of personality traits in terms of five basic dimensions: Extraversion, Agreeableness,
Conscientiousness, Neuroticism, and Openness to Experience (McCrae & John, 1992, p. 175). FFM is categorized
as being in or associated with the “trait theory” of personality (p. 199). Personality traits correlate with technology
use. For example Butt and Phillips (2008, p. 348) state that neurotics are “using the Internet to feel part of a group
and to escape loneliness” whereas Kraut et al. (2002) report that “Internet use with changes in community
involvement was positive for extraverts and negative for introverts” (p. 61). Moreover, Schrammel et al. (2009) state
that “interest of using the internet for communication” is high for those “with high levels on neuroticism” (p. 170).
When exploring human behavior in internet communication, personality and motivation go hand in hand. On the
relationship of personality and motivation Ewen (2003) states that “personality is a comprehensive construct and
motivation is a fundamental aspect of behavior … Therefore, theories of personality are in large part theories of
motivation” (p.6). On the other hand, FB is a communication tool and “it is necessary to take into consideration a
person’s motivation for communication” (Spitzberg, 2006, p. 580).

SNs can have a profound impact on learning experience. Regarding computer science students, Liccardi et al. (2007,
pp. 224-226) highlights many roles of SNs in learning experience, especially in the context of “pedagogies of
social-cultural theories of learning” (p. 226). They argue that SNs can act as a pedagogical agent with
problem-based learning (p. 224) and can be construed as communities of practice (p. 226) and can assist educators
with their teaching (p. 225). SNSs are software which builds on SNs. Therefore, the nature of an SNS is inherently
interpersonal. This interpersonal nature, according to McKenna et al. (2002) causes “many relationships formed
online” to “eventually result in real world contact” (p. 28). They state that SNSs demonstrate an “online-to-offline”
trend in the meeting of the people indicating the impact of SNSs on the lives of individuals. The striking point,
however, is that, according to Ross et al. (2009), FB “tends to demonstrate opposite progression” (p. 578). The
possibility of carrying over the negative aspects of “offline” SNs to the learning environment will always exist;
however, FB has the potential to carry all the positive effects of “offline” SNs to an “online” platform.

SN is a key issue for teaching, teachers and, thus, teacher training. Coburn et al. (2010) state that “teachers’ [SNs]
are an important part of the school improvement puzzle” (p. 33). They argue that the nature and quality of SNs are
associated with “a myriad of outcomes that are central to instructional change and school improvement.” They
indicate that “[SNs] with strong ties can facilitate diffusion of innovation, transfer of complex information, and
increased problem solving.” Moreover, Daly (2010) states that, SN theory “provides insight into motives of resisters
to change, and spheres of social influence” (p. 3). Regarding teaching and SNs, Atteberry et al. (2010) state that SNs
“play a key role in understanding the degree of success schools experience in terms of improvements for teachers
and students” (p. 73). SNSs can have a positive effect on teacher professional development as well. Baker-Doyle et
al (2010) argue that using SNS increases collaboration (p. 119) and “teachers communicate with each other more
frequently on the [SNS] during the school year to share resources, request directed help from peers for both
curricular and technological instruction, and connect about difficulties experienced in implementation efforts” (p.
124). Therefore, SN and SNSs can play a crucial role in teacher training considering their positive effect on
preservice teachers’ future jobs regarding implementation, improvement and development of teachers’ professions.

The Study
The purpose of the research is to identify and analyze the association of preservice teachers’ personality, motives,
motivation, and attitudes with the use of SNSs (specifically, FB). To investigate those relationships, four instruments
were used to measure psychological constructs and demographics: “NEO Five Factor Inventory,” “CMC Motivation
Scale,” “Facebook Motives Scale,” and “Facebook Attitudes Scale.” All of the instruments had previously been
developed and used for earlier research and were originally developed in the English language. One of the
instruments (NEO Five Factor Inventory) was previously translated into Turkish by a Turkish researcher. The other
three were translated into Turkish by the author of this paper and tested in a pilot study. In the actual study, only the
Turkish versions were used. Because of relative advantages of an online survey compared with paper-based survey,
a free (software) and online survey application (Limesurvey) was used to conduct the survey. This research study
complies with the Ethical Principles of Psychologists and Code of Conduct set by the American Psychological
Association (2010) and ethical standards set by the Research Center for Applied Ethics of METU and the entire
study has been reviewed by Human Subjects Ethics Committee of METU.

NEO Five Factor Inventory (NEO-FFI) is a personality scale developed by Robert R. McCrae and Paul T. Costa and
first published in 1985. NEO FFI is a shortened (60 item) version of the revised NEO Personality Inventory (NEO
PI-R) which consists of 240 items. The scale is based on trait theory of personality and an “operationalization of
the Five-Factor Model (FFM), which structures specific traits in terms of five broad factors” (Costa
et al., 2001, 322). The scale is designed to “measure the five factors of personality: Neuroticism (N), Extraversion
(E), Openness to Experience (O), Agreeableness (A), and Conscientiousness (C). Items in NEO FFI are answered on
a 5 point Likert scale, ranging from “strongly agree” (1) to “strongly disagree” (5). It was translated into Turkish by
Ersin Kuşdil for his doctoral thesis research (Kuşdil, 2000, p. 147). Computer Mediated Communication
Competence Measure version 5 (CMC competence measure) was developed by Brian H. Spitzberg (Spitzberg, 2006,
p. 629). Ross et al. (2009) used three factors of Spitzberg’s CMC (Computer Mediated Communication) competence
measure in their research: “Motivation,” “Knowledge,” Efficacy” (p. 580) and reported that only the “motivation”
(M) factor was correlated with FB usage (p. 581). Ross et al. (2009) reports that the “reliability for the three domains
is acceptable (from a = .73 to a = .90)” (p. 580). Thus, even though only the motivation factor seems to be necessary
for this research, just to be cautious, it was decided that three factors of Spitzberg’s measure as used by Ross et al.
were to be used. The measure consists of 18 items being answered on a 5 point Likert scale, ranging from “not at all
true of me” (1) to “very true of me” (5). Facebook Motives Scale was first developed by Pavica Sheldon (2008, p.
44) based on gratification theory. She used 38 items and extracted 6 factors of which the Eigen values are greater
than 1.0 and altogether are accounted for 60 percent of the variance. Cronbach’s alpha values ranged from 0.75 to
0.90 (p. 47). The items were answered on a 5 point Likert scale, ranging from “not at all” (1) to “exactly” (5).
Facebook Attitude Scale was extracted from the Facebook Questionnaire developed by Ross et al (2009, p.580). The
questionnaire consists of 28 items and the scale includes an attitudes factor comprising of 7 items. Items are being
answered on a 5 point Likert scale, ranging from “strongly agree” (1) to “strongly disagree” (5).

Since this research is aimed to identify and analyze existing relationships among existing variables, correlational
research was considered as the appropriate method of investigation. After factor analyses, predictor variables were
“attitude towards using FB,” “motives to use FB” (Virtual community: V, Relationship maintenance: R, Passing
time: P), “motivation to use CMC” (M), “personality” (E, A, C, N, and O). Therefore 10 psychological constructs
constituted the set of predictor variables. The criterion variables were demographic information about the use of FB.

The sampling method was convenience sampling. The target population of the study is all the preservice teachers in
Turkey. The accessible population is all the preservice teachers who are currently enrolled in the Faculty of
Education of METU in Turkey. The sample is the group of individuals who participated in the study and who have a
FB account. There are seven undergraduate programs in the Faculty of Education of METU. The total number of the
undergraduate students is 1510 and all of them were asked to participate in the study. Of this 1510, 229 were
enrolled in the department in which the author of this paper is a research assistant: Computer Education and
Instructional Technology (CEIT). Two hundred twenty nine CEIT students were reserved for the pilot study and the
remaining 1282 students were asked to participate in the actual study.


One hundred seventy nine of 229 students participated in the pilot study and 136 of them had a FB account. Only
those who have a FB account (Npilot=136) could complete the survey (Table 1). In the actual study 780 of 1282
students participated in the study and 641 of them had an FB account (Nactual=641, Table 1). The actual sample was
comprised of 518 female and 123 male students, having an average age of 21.29 years (SD=1.855). For conducting
statistical analysis a specific computer software program -IBM SPSS v19.0.0 (www.spss.com)- was used. After the
pilot study, all of the instruments except NEO-FFI-TR were revised according to Principal Component Analysis
(PCA) results. In the actual study, the revised versions of the instruments were used and after collecting the data, a
second PCA with Varimax rotation was conducted. A reliability analysis was conducted for the factor groups and
factor variables were constructed by regression coefficients produced by the Factor Analysis procedures of SPSS.

                         Gender        Accessible     Responded          Completed         Completion
                                                                                              59.34 %
             Pilot                        229             179                136
                                                                                           of Accessible
                      Male (%)                       129 (27.9)        96 (70.6)
                      Female (%)                     50 (72.1)         40 (29.4)
                                                                                               50 %
            Actual                        1282            780                641
                                                                                           of Accessible
                      Male (%)                       160 (20.5)      123 (19.2)
                      Female (%)                     620 (79.5)      518 (80.8)
                                                Table 1. Demographics.

Consistent with previous research (Ross et al., 2009, p. 581) 82.12% of the participants have a FB account. 41.5%
had an account for more than 3 years and the majority (60.7%) spends 30 to 180 minutes on FB daily. Another
remarkable fact was related to privacy: 85.5% reported that only their friends can see their profiles. To investigate
the relationships, 4 regression procedures were conducted by using SPSS. Each regression received 1 dependent
variable (demographics regarding FB use) and 10 independent variables. For ordinal dependents (Duration,
Intensity), Ordinal Logistic Regression was used whereas a Multinomial Logistic Regression was used for the
categorical dependent variable (ProfileSee). The only continuous dependent variable –FriendCount- was assessed in
the Multiple Linear Regression (See Table 2 for results summary).

First, an ordinal logistic regression was conducted between predictor variables and Duration (Approximately how
long have you had your Facebook profile?) (χ = 78.680, df=10, p<0,001). Neuroticism (Wald=3.940, df=1, p<0.05;

exp(B)=1.16), Openness to Experience (Wald=8.461, df=1, p<0.05; exp(B)=1.24), Attitude (Wald=8.052, df=1,
p<0.01; exp(B)=1.32), Motivation (Wald=5.787, df=1, p<0.05; exp(B)=1.24) and Passing Time (Wald=9.726, df=1,
p<0.05; exp(B)=1.32) predicted the Duration successfully. The results indicate that people didn’t rush to FB for
finding friends. Interestingly, it started as a means for users who were seeking a way to pass time. Moreover, attitude
has the highest effect size (exp(B)=1.32). Results indicate that having motivation and attitude predicts having a FB
account earlier.

Second, another ordinal logistic regression was conducted between predictor variables and Intensity (On average,
approximately how many minutes per day do you spend on Facebook?) ( χ =353.114, df=10, p<0.001). Extraversion

(Wald=6.818, df=1, p<0.01; exp(B)=1.22), Neuroticism (Wald=4.108, df=1, p<0.05; exp(B)=1.16), Openness to
Experience (Wald=11.884, df=1, p<0.001; exp(B)=1.29), Attitude (Wald=74.614, df=1, p<0.000; exp(B)=2.38),
Motivation (Wald=9.760, df=1, p<0.005; exp(B)=1.31), Passing time (Wald=55.633, df=1, p<0.000; exp(B)=2.00)
predicted the Intensity. Strikingly, attitude towards FB has the strongest effect size (exp(B)=2.38) followed by
Passing time (exp(B)=2.00) indicating that even though there is a significant relationship between personality traits
and intensity of FB use, personality is not the main factor influencing its use. So that educational uses of FB or SNSs
in general are feasible. Another point is that neurotics and those who are open to experience are both predicting the
Duration and Intensity.

Third procedure was a multinomial logistic regression between ProfileSee (Who can see your Facebook profile?)
and predictors (χ =60.303, df=30, p<0.001). The model successfully predicted %85.3 of cases. This procedure

produced 3 sets of results associated with the levels of the dependent variable. In the second level of ProfileSee (All
Networks and Friends), only the motives predicted the change in outcome: Passing Time (Wald=5.904, df=1,
p<0.05; exp(B)=0.602), Relationship (Wald=6.542, df=1, p<0.05; exp(B)=0.607), Friendship (Wald=10.362, df=1,
p<0.01, exp(B)=1.726). The regression used the first level of the ProfileSee variable (Only my friends) to refer first.
Thus, results indicate that motives are important factors regarding the privacy issues. In the third level of ProfileSee
(Some networks/all friends) there was no significant prediction. Result shows that the respondents dichotomize their
contacts as “Only my friends” and “All Networks and Friends.” The last level (I Don’t know), had two personality
traits and one motive dimension: Neuroticism (Wald=6.793, df=1, p<0.01; exp(B)=1.971), Conscientiousness
(Wald=4.097, df=1, p<0.05, exp(B)=0.602), Passing (Wald=5.131, df=1, p<0.05; exp(B)=0.495).

                                            Duration        Intensity         ProfileSee       FriendCount
         Model                             χ2 = 78.680       2
                                                           χ =353.114         χ2 =60.303         R2=0.183
         Extraversion                            -        exp(B)=1.22              -           +0.223
         Agreeableness                           -              -                  -           –0.998
         Conscientiousness                       -              -          exp(B3)=0.602             -
         Neuroticism                      exp(B)=1.16     exp(B)=1.16      exp(B3)=1.971             -
         Openness to Experience           exp(B)=1.24     exp(B)=1.29              -           +0.107
         Friendship                              -              -          exp(B2)=1.726             -
         Relationship                            -              -          exp(B2)=0.607       +0.102
         Passing Time                    exp(B)=1.32 exp(B)=2.00                               +0.099
         Motivation                      exp(B)=1.24 exp(B)=1.31                   -                 -
         Attitude                        exp(B)=1.32 exp(B)=2.38                   -           +0.197
                                       Table 2. Summary of statistical results.
Finally, a multiple linear regression was conducted between FriendCount (Approximately how many friends are on
your Facebook Friends List?) and the predictor variables (F(10.622) = 13.921, p <0.001). All independent variables
entered in the regression. The standardized regression equation (Y1= 0.223*E–0.998*N+0.107*O+0.197*A+0.099*
P+0.102*R) included 3 personality traits (E, N, O), attitude, and two motive dimensions (P, R). The sample multiple
correlation coefficient was 0.428 and approximately 20% of the variance on the number of FB friends is accounted
for Extraversion, Neuroticism, Openness to Experience, Attitude towards FB use, Passing Time, and Relationship
(R2=0.183). The results indicate that personality traits are more important predictors of the “social” side of FB
followed by attitude towards FB use.

The purpose of this paper was to investigate the relationship of personality, motivation, attitude, and motive related
factors with FB use. In parallel with previous research (Ross et al., 2009, p. 582) personality traits were associated
with FB use. Especially Neuroticism, Extraversion and Openness to Experience yielded significant prediction results
in regression analysis. Motive to use FB and attitude towards FB use also are associated with FB use. Extraversion
and Neuroticism are influential in the number of friends. While extraverts have a positive relationship with the
number of friends, Neurotics have stronger but negative relationship with number of friends. Results also indicate
that neurotic and conscientious students tend to have privacy concerns. Contrasting with the findings on the number
of friends neurotics tend to use FB more often. Extraverts and those students who are “open to experience” are using
FB often too. These results indicate that personality is a major issue regarding the educational uses of FB. The
ongoing and future efforts to develop educational modules on or in the FB infrastructure or those teachers who are
using or considering to directly using FB for daily educational activities should consider that personality differences
highly influence the way students use it.

On the other hand, for some aspects, personality traits are less important than other factors. Results indicate that
motive to pass time and attitude towards FB use were more influential on those students who were first and most
attracted to FB. Motives and attitudes are more influential on the time spent on FB. This result, actually, is a
promising result for scholars, teachers, and developers who are considering SNSs as educational environments. The
results on the frequency of using FB indicate that personality doesn’t constitute a major problem for teachers and
developers since ways for helping students to develop a positive attitude towards SNSs are probable. Another
significant finding was that the motives to use FB are highly associated with the privacy concerns of the students.
Considering the natural properties of an educational environment, teachers and developers should seriously take the
privacy concerns into account. In parallel with previous research (Ross et al., 2009, p. 582; Spitzberg, 2006, p. 639),
motivation is highly associated with FB use. All statistical analysis except the one predicting the number of friends
revealed high associations of FB use with motivation construct. This is another promising finding regarding the
educational uses of SNSs and FB in particular. The findings regarding “motivation to use CMC” and “attitude
towards FB use” reveal that, since these factors are subject to improvement, teachers may have a chance to better
keep students “in” the learning environment.

The study resulted in important findings regarding teacher training. As previously stated, regarding the teachers,
SNSs have the potential to influence the diffusion of innovation, social influence, and receiving help from peers.
The results indicate that use of FB correlates with personality traits. Therefore, considering their low profile on SNS
environment, especially, neurotic preservice teachers are more likely to fail to benefit from SNSs in their future
professions. On the other hand, parameters of use of FB correlate with motives and attitude, as well. Therefore, it
should be noted that the nature and quality of SNS coverage in teacher training programs will influence teachers’
professional development in the future regarding perceptions, beliefs, and readiness of teachers about SNSs. Finally,
emergence of privacy concerns in the implementation of FB indicates that a possible gender difference might
emerge in terms of benefits that a teacher may gain from SNSs while working, especially regarding professional
development. Considering female individuals’ privacy concerns compared to male counterparts, teacher training
programs should take into account placing more emphasis on learning to utilize and use of SNSs by female
preservice teachers. SNS developers, policy makers, and educational administrators should consider privacy
concerns of preservice teachers as well as students while developing and implementing SNS applications.
Further Research
This paper reports the findings from the quantitative part of ongoing research. Qualitatively studying the use of FB
is necessary for better understanding the possibility of educational use of FB. SNSs, by definition, are based on and
simulate social structures, and cultural differences are even more significant when the learning environments require
human-computer interaction. Thus, another important contribution would be investigating the phenomenon by a
cross-cultural research design.

Atteberry, A., Bryk, A. S. (2010). Centrality, connection, and commitment: The role of social networks in a
school-based literacy initiative. In A. J. Daly (Ed.), Social network theory and educational change (pp. 51-75).
Cambridge, MA, USA: Harvard Education Press.

Baker-Doyle, K. J., Yoon, S. A. (2010). Making expertise transparent: Using technology to strengthen social
networks in teacher professional development. In A. J. Daly (Ed.), Social network theory and educational change
(pp. 115-126). Cambridge, MA, USA: Harvard Education Press.

Boyd, D. M. & Ellison, N. B. (2008). Social network sites: Definition, history and scholarship. Journal of
Computer-Mediated Communication, 13(1), 210-230.

Butt, S. and Phillips, J. G. (2008). Personality and self reported mobile phone use. Computers in Human Behavior,
24(2), 346-360.

Coburn, C. E., Choi, L., Mata, W. (2010). “I would go to her because her mind is math”: Network formation in the
context of a district-based mathematics reform. In A. J. Daly (Ed.), Social network theory and educational change
(pp. 33-50). Cambridge, MA, USA: Harvard Education Press.

Daly, A. J. (2010). Mapping the terrain: Social network theory and educational change. In A. J. Daly (Ed.), Social
network theory and educational change (pp. 1-16). Cambridge, MA, USA: Harvard Education Press.

Dutta, S., Mia, I., Geiger, T., Herrera, E. T. (2010). How networked is the world? Insights from the networked
readiness index 2009–2010. In S. Dutta & I. Mia (Eds.), The global information technology report 2001–2002:
Readiness for the networked world, pp. 3-30. Geneva: World Economic Forum. Retrieved on October 3, 2010 from

Ewen, R. B. (2003). An introduction to theories of personality, (6th ed.). New Jersey, USA: Lawrence Erlbaum

Facebook. (2012). Statistics. Retrieved on Jenuary 19, 2012 from http://www.facebook.com/press/info.php?statistics

Hornaday, A. (2010). The social network: A universal story that's hard not to like. Washington Post. Retrieved from

Kirschner, P. A. and Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human
Behavior, 26(6), 1237-1245.
Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox revisited.
Journal of Social Issues, 58, 49–74.

Kuşdil, M.E. (2000). Value-socialisation in cultural context: A study with British and Turkish families.
(Unpublished doctoral dissertation).University of Sussex at Brighton, United Kingdom.

Liccardi, I., Ounnas, A., Pau, R., Massey, E., Kinnunen, P., Lewthwaite, S., Midy, M., Sarkar, C. (2007). The role of
social networks in students' learning experiences. In ITiCSE-WGR ’07: Working group reports on ITiCSE on
Innovation and technology in computer science education (pp. 224-237). New York, NY, USA: ACM.

Lipsman, A. (2010, November 8). U.S. online display advertising market delivers 22 percent increase in impressions
vs. year ago. Retrieved November 12th, 2010, from

McCrae, R. R., John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of
Personality, 60(2), 175-215.

McKenna, Katelyn Y.A., Green,A. S., & Glenson, Marci E.J.(2002). Relationship formation on the Internet: What’s
the big attraction? Journal of Social Issues, 58(1), 9–31.

National School Boards Association (NSBA). (2007). Creating and connecting: Research and guidelines on social
and educational networking. Alexandria, VA, USA: NSBA.

Puig, C. (2010). 'Social Network' puts story of Facebook into the big picture. USA Today. Retrieved from

Rickey, C. (2010). An engrossing portrait of Facebook and its creators. The Philadelphia Inquirer. Retrieved from

Ross, C., Orr, E.S., Sisic, M., Arseneault, J.M., Simmering, M.G., Orr, R.R. (2009). Personality and motivations
associated with Facebook use. Computers in Human Behavior, 25(2), 578-586.

Schrammel, J., Köffel, C., Tscheligi, M. (2009). Personality traits, usage patterns and information disclosure in
online communities. In Proceedings of the 23rd British HCI Group Annual Conference on People and Computers:
Celebrating People and Technology (BCS-HCI '09) (pp. 169-174). Swinton, UK: British Computer Society.

Sheldon, P. (2008). Student favorite: Facebook and motives for its use. Southwestern Journal of Mass
Communication, 23(October), 39-54.

Spitzberg, G. H. (2006). Preliminary development of a model and a measure of computer mediated communication
(CMC) competence. Journal of Computer Mediated Communication, 11, 629–666.

Wilson, C. (2010). Story of Facebook makes for a top film. St. Louis Post-Dispatch. Retrieved from

To top